The Darjeeling Himalayan region,characterized by its complex topography and vulnerability to multiple environmental hazards,faces significant challenges including landslides,earthquakes,flash floods,and soil loss that...The Darjeeling Himalayan region,characterized by its complex topography and vulnerability to multiple environmental hazards,faces significant challenges including landslides,earthquakes,flash floods,and soil loss that critically threaten ecosystem stability.Among these challenges,soil erosion emerges as a silent disaster-a gradual yet relentless process whose impacts accumulate over time,progressively degrading landscape integrity and disrupting ecological sustainability.Unlike catastrophic events with immediate visibility,soil erosion’s most devastating consequences often manifest decades later through diminished agricultural productivity,habitat fragmentation,and irreversible biodiversity loss.This study developed a scalable predictive framework employing Random Forest(RF)and Gradient Boosting Tree(GBT)machine learning models to assess and map soil erosion susceptibility across the region.A comprehensive geo-database was developed incorporating 11 erosion triggering factors:slope,elevation,rainfall,drainage density,topographic wetness index,normalized difference vegetation index,curvature,soil texture,land use,geology,and aspect.A total of 2,483 historical soil erosion locations were identified and randomly divided into two sets:70%for model building and 30%for validation purposes.The models revealed distinct spatial patterns of erosion risks,with GBT classifying 60.50%of the area as very low susceptibility,while RF identified 28.92%in this category.Notable differences emerged in high-risk zone identification,with GBT highlighting 7.42%and RF indicating 2.21%as very high erosion susceptibility areas.Both models demonstrated robust predictive capabilities,with GBT achieving 80.77%accuracy and 0.975 AUC,slightly outperforming RF’s 79.67%accuracy and 0.972 AUC.Analysis of predictor variables identified elevation,slope,rainfall and NDVI as the primary factors influencing erosion susceptibility,highlighting the complex interrelationship between geo-environmental factors and erosion processes.This research offers a strategic framework for targeted conservation and sustainable land management in the fragile Himalayan region,providing valuable insights to help policymakers implement effective soil erosion mitigation strategies and support long-term environmental sustainability.展开更多
Paleoenvironmental reconstruction is fundamental to understand the modern environmental changes and to predict future environment, which is especially critical to understand the evolution of land and sea during geolog...Paleoenvironmental reconstruction is fundamental to understand the modern environmental changes and to predict future environment, which is especially critical to understand the evolution of land and sea during geological periods. However, the basic geological research on China＇s muddy coastal zone is not enough to provide quantitative data to compare with global changes. Therefore, in 2011, China Geological Survey deployed the ＂Late Quaternary geo-environmental evolution and modern process of China＂ project, and focused on the muddy coastal zones of the Liaodong Bay, Bohai Bay, the Yellow River Delta, Yangtze River Delta and Pearl River Delta （Fig. 1）. Next we will briefly introduce our latest results in the Bohai Bay.展开更多
The Wenchuan earthquake in 2008 and geo-hazards triggered by the earthquake caused large injuries and deaths as well as destructive damage for infrastructures like construction, traffic and electricity. It is urgent t...The Wenchuan earthquake in 2008 and geo-hazards triggered by the earthquake caused large injuries and deaths as well as destructive damage for infrastructures like construction, traffic and electricity. It is urgent to select relatively secure areas for townships and cities constructed in high mountainous regions with high magnitude earthquakes. This paper presents the basic thoughts, evaluation indices and evaluation methods of geological security evaluation, water and land resources security demonstration and integrated assessments of geo-environmental suitability for reconstruction in alp and ravine with high magnitude earthquakes, which are applied in the worst-hit areas （12 counties）. The integrated assessment shows that： （1） located in the Longmenshan fault zone, the evaluated area is of poor regional crust stability, in which the unstable and second unstable areas account for 79% of the total; （2） the geo-hazards susceptibility is high in the evaluation area. The spots of geo-hazards triggered by earthquake are mainly distributed along the active fault zone with higher distribution in the moderate and high mountains area, in which the areas of high and moderate susceptibility zoning accounts for 40.1% of the total; （3） geological security is poor in the evaluated area, in which the area of the unsuitable construction occupies 73.1%, whereas in the suitable construction area, the areas of geological security, second security and insecurity zoning account for 8.3 %, 9.3% and 9.3 % of the evaluated area respectively; （4） geo-environmentai suitability is poor in the evaluated area, in which the areas of suitability and basic suitability zoning account for 3.5% and 7.3% of the whole evaluation area.展开更多
In recent years, a large number of geotechnical engineering projectshave been completed or under construction in China, such asthe Three Gorges Dam Project, Expressway Network Plan, South-to-North Water Diversion Proj...In recent years, a large number of geotechnical engineering projectshave been completed or under construction in China, such asthe Three Gorges Dam Project, Expressway Network Plan, South-to-North Water Diversion Project, West-to-East Gas Pipeline Project,etc. （Wang, 2003; Li, 2010; Huang, 2011; She and Lin, 2014）. Theconstruction of large-scale geotechnical engineering not onlybrings huge economic benefits, but also causes large interferenceto the lithosphere and hydrosphere that we rely on for survival（Wang et al., 2005）. This paper focuses on the interaction mechanismof rock engineering and geo-environments in the fields of urbanunderground space utilization, natural gas hydrate exploitationand high-level radioactive waste disposal.展开更多
A geo-environmental investigation is carried out to identify the suitability for treatment,storage and disposal facility(TSDF) in the industrial area at Perundurai,Tamilnadu(India).State industries promotion corporati...A geo-environmental investigation is carried out to identify the suitability for treatment,storage and disposal facility(TSDF) in the industrial area at Perundurai,Tamilnadu(India).State industries promotion corporation of Tamilnadu(SIPCOT), Perundurai is a fast growing industrial centre therefore,needs a common utility i.e.TSDF site for safe management of the industrial wastes.展开更多
Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,y...Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.展开更多
文摘The Darjeeling Himalayan region,characterized by its complex topography and vulnerability to multiple environmental hazards,faces significant challenges including landslides,earthquakes,flash floods,and soil loss that critically threaten ecosystem stability.Among these challenges,soil erosion emerges as a silent disaster-a gradual yet relentless process whose impacts accumulate over time,progressively degrading landscape integrity and disrupting ecological sustainability.Unlike catastrophic events with immediate visibility,soil erosion’s most devastating consequences often manifest decades later through diminished agricultural productivity,habitat fragmentation,and irreversible biodiversity loss.This study developed a scalable predictive framework employing Random Forest(RF)and Gradient Boosting Tree(GBT)machine learning models to assess and map soil erosion susceptibility across the region.A comprehensive geo-database was developed incorporating 11 erosion triggering factors:slope,elevation,rainfall,drainage density,topographic wetness index,normalized difference vegetation index,curvature,soil texture,land use,geology,and aspect.A total of 2,483 historical soil erosion locations were identified and randomly divided into two sets:70%for model building and 30%for validation purposes.The models revealed distinct spatial patterns of erosion risks,with GBT classifying 60.50%of the area as very low susceptibility,while RF identified 28.92%in this category.Notable differences emerged in high-risk zone identification,with GBT highlighting 7.42%and RF indicating 2.21%as very high erosion susceptibility areas.Both models demonstrated robust predictive capabilities,with GBT achieving 80.77%accuracy and 0.975 AUC,slightly outperforming RF’s 79.67%accuracy and 0.972 AUC.Analysis of predictor variables identified elevation,slope,rainfall and NDVI as the primary factors influencing erosion susceptibility,highlighting the complex interrelationship between geo-environmental factors and erosion processes.This research offers a strategic framework for targeted conservation and sustainable land management in the fragile Himalayan region,providing valuable insights to help policymakers implement effective soil erosion mitigation strategies and support long-term environmental sustainability.
基金funded by China Geological Survey(Grants No.1212011120169 and 12120113005800)the National Natural Science Foundation of China(Grants No.41206069,41476074 and 41372173)
文摘Paleoenvironmental reconstruction is fundamental to understand the modern environmental changes and to predict future environment, which is especially critical to understand the evolution of land and sea during geological periods. However, the basic geological research on China＇s muddy coastal zone is not enough to provide quantitative data to compare with global changes. Therefore, in 2011, China Geological Survey deployed the ＂Late Quaternary geo-environmental evolution and modern process of China＂ project, and focused on the muddy coastal zones of the Liaodong Bay, Bohai Bay, the Yellow River Delta, Yangtze River Delta and Pearl River Delta （Fig. 1）. Next we will briefly introduce our latest results in the Bohai Bay.
文摘The Wenchuan earthquake in 2008 and geo-hazards triggered by the earthquake caused large injuries and deaths as well as destructive damage for infrastructures like construction, traffic and electricity. It is urgent to select relatively secure areas for townships and cities constructed in high mountainous regions with high magnitude earthquakes. This paper presents the basic thoughts, evaluation indices and evaluation methods of geological security evaluation, water and land resources security demonstration and integrated assessments of geo-environmental suitability for reconstruction in alp and ravine with high magnitude earthquakes, which are applied in the worst-hit areas （12 counties）. The integrated assessment shows that： （1） located in the Longmenshan fault zone, the evaluated area is of poor regional crust stability, in which the unstable and second unstable areas account for 79% of the total; （2） the geo-hazards susceptibility is high in the evaluation area. The spots of geo-hazards triggered by earthquake are mainly distributed along the active fault zone with higher distribution in the moderate and high mountains area, in which the areas of high and moderate susceptibility zoning accounts for 40.1% of the total; （3） geological security is poor in the evaluated area, in which the area of the unsuitable construction occupies 73.1%, whereas in the suitable construction area, the areas of geological security, second security and insecurity zoning account for 8.3 %, 9.3% and 9.3 % of the evaluated area respectively; （4） geo-environmentai suitability is poor in the evaluated area, in which the areas of suitability and basic suitability zoning account for 3.5% and 7.3% of the whole evaluation area.
文摘In recent years, a large number of geotechnical engineering projectshave been completed or under construction in China, such asthe Three Gorges Dam Project, Expressway Network Plan, South-to-North Water Diversion Project, West-to-East Gas Pipeline Project,etc. （Wang, 2003; Li, 2010; Huang, 2011; She and Lin, 2014）. Theconstruction of large-scale geotechnical engineering not onlybrings huge economic benefits, but also causes large interferenceto the lithosphere and hydrosphere that we rely on for survival（Wang et al., 2005）. This paper focuses on the interaction mechanismof rock engineering and geo-environments in the fields of urbanunderground space utilization, natural gas hydrate exploitationand high-level radioactive waste disposal.
文摘A geo-environmental investigation is carried out to identify the suitability for treatment,storage and disposal facility(TSDF) in the industrial area at Perundurai,Tamilnadu(India).State industries promotion corporation of Tamilnadu(SIPCOT), Perundurai is a fast growing industrial centre therefore,needs a common utility i.e.TSDF site for safe management of the industrial wastes.
基金the National Natural Science Foundation of China(42377170,42407212)the National Funded Postdoctoral Researcher Program(GZB20230606)+3 种基金the Postdoctoral Research Foundation of China(2024M752679)the Sichuan Natural Science Foundation(2025ZNSFSC1205)the National Key R&D Program of China(2022YFC3005704)the Sichuan Province Science and Technology Support Program(2024NSFSC0100)。
文摘Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.
